Cellular heterogeneity and patterning strategies as revealed by upper respiratory epithelium single cell atlas

Alexander G Foote; Xin Sun

doi:10.1016/j.isci.2025.112845

. 2025 Jun 7;28(7):112845. doi: 10.1016/j.isci.2025.112845

Cellular heterogeneity and patterning strategies as revealed by upper respiratory epithelium single cell atlas

Alexander G Foote ¹, Xin Sun ^1,^2,^3,^∗

PMCID: PMC12221713 PMID: 40612507

Summary

The upper respiratory tract, spanning the pharyngolaryngeal to tracheobronchial regions, enables breathing, vocalization, and frontline defense against airborne insults. We generated a cellular and molecular atlas of the mouse upper respiratory epithelium from pharynx/larynx to tracheobronchial carina by combining single-cell RNA sequencing with spatial validation. Our analysis revealed 18 epithelial cell types, organized into three spatially distinct compartments: Tmprss11a+ pharyngolaryngeal, Nkx2-1+ tracheobronchial, and Dmbt1+ submucosal glands. Stratified squamous pharyngolaryngeal zones displayed extensive and region-specific Keratin codes. Within the pseudostratified tracheobronchial epithelium, diverse luminal cells, including multiple varieties of club cells, exhibit marker-expression gradients along the proximal–distal axes. Lastly, analysis of the submucosal gland epithelium —which contains various cell types, including distinctive myoepithelial cells— revealed extensive diversity both among and within its cellular populations. This spatially resolved transcriptomic atlas elucidates how epithelial identity varies along the upper respiratory axes and will guide investigations into cellular dynamics in health and disease.

Subject areas: Cellular physiology, Organizational aspects of cell biology, Biology of human development, Complex systems

Graphical abstract

Highlights

•
18 cell types in pharyngolaryngeal, tracheobronchial, and submucosal regions
•
Keratin codes and “stress-induced” KRT6A/KRT17 at homeostasis
•
Distinct basal and luminal cell characteristics along the proximal-distal axis
•
Markers for diverse submucosal gland cells including mucous and myoepithelial cells

Cellular physiology; Organizational aspects of cell biology; Biology of human development; Complex systems

Introduction

The respiratory tract encompasses the nasal cavity, pharynx/larynx (throat), trachea, bronchi, and lungs, operating as a connected and interdependent system. Mucosal surfaces lined by epithelial cells are essential elements of the respiratory tract, effective not only as a first-line physical barrier against chronic external threats but also for host immune defense, and injury repair.¹^,²^,³^,⁴ The epithelium is vital for maintaining respiratory health. Conversely, it is also the site for pathogenesis of an array of upper airway acute and chronic conditions, such as laryngitis/pharyngitis, bronchitis, chronic cough, croup, etc.⁵^,⁶^,⁷ While substantial research has uncovered cellular diversity of the lower bronchopulmonary airways,⁸ comprehensive characterization of epithelial cell populations and their transcriptomic profiles within the lower pharyngeal and laryngeal regions, extending distally toward the tracheobronchial carina, remains limited. Given that respiratory diseases frequently exhibit regional specificity in their clinical manifestation, understanding epithelial cell heterogeneity at these specific anatomical locations is critical.

Research on the proximal conducting tracheal airway has identified significant cellular heterogeneity and revealed diverse roles in maintaining homeostasis⁸^,⁹^,¹⁰^,¹¹ and facilitating repair.³^,⁸^,¹⁰^,¹¹^,¹² Current evidence suggests that the larynx plays a significant role in shaping host immunity and thus mediates respiratory health and disease.¹³ Yet, the laryngeal transcriptome remains relatively underexplored at the single cell level.¹³ Moreover, previous studies have predominantly focused on single-organ analyses, with no investigations to date that comprehensively examine both the trachea and larynx in tandem. While commonly known cell types such as basal, ciliated, mucous/goblet, and club cells are widely present in mucosal regions of the trachea and portions of the larynx (e.g., superior vocal fold region and the inferior laryngeal aspect of the epiglottis), their transcriptomic profiles have not been fully characterized to determine whether significant differences exist between these anatomically distinct areas.

Submucosal glands (SMGs) are primarily localized within the larynx, with a smaller reservoir situated in the ventral proximal trachea. These glands play a pivotal role in airway innate immunity by regulating the secretion of antimicrobial peptides and mucins essential for mucosal defense.¹⁴^,¹⁵^,¹⁶ Additionally, recent studies have identified rare cell types with unique chemosensory and immune-mediated functions, including neuroendocrine cells,¹⁷ tuft cells,¹⁸^,¹⁹ and region-specific cells such as hillock structures.²⁰ For example, tuft cells, known as brush cells in the trachea and solitary chemosensory cells in the nasal, pharyngeal, and laryngeal epithelium, exhibit dual roles in immune and chemosensory effector functions.¹⁹^,²¹^,²²^,²³^,²⁴ Neuroendocrine cells function as specialized sensory epithelial cells that protect the airways by releasing ATP to activate sensory neurons, triggering critical reflexes such as swallowing and expiration.¹⁷ Furthermore, unique tracheal hillock structures have been identified as injury-resistant reservoirs with distinct regenerative potential.²⁰

With the variations in cell spatial patterning and the functional diversity of cell types, we hypothesized that the larynx and trachea possess unique, organ-specific transcriptomic profiles reflective of their specialized cell populations and tissue microenvironments. To analyze the cell diversity, we generated a comprehensive dataset covering the pharyngolaryngeal-to-tracheobronchial axis and established a single-cell atlas for the mouse adult upper respiratory epithelium. Our single-cell RNA-seq (scRNA-seq) analysis revealed 18 cell types across three spatially distinct compartments; (1) Tmprss11a+ stratified squamous epithelium (SSE) of the larynx, (2) Nkx2-1+ pseudostratified epithelium of the trachea, and (3) Dmbt1+ SMG epithelium. Our dataset is rich with cell diversity, including abundant cell types, club (33%), basal (29%), and mucous-2-SMG (11%), as well as rare, specialized cell types, including mucous-larynx (1%), tuft (0.5%), and neuroendocrine (0.1%) cells. We further separated basal cell transcriptomic signatures and gene-ontology analysis based upon respective compartment (trachea-dorsal:Trp63+Cav1+), (trachea-ventral:Trp63+Tgm2+), (larynx:Trp63+Igfbp2+Tmprss11a+), and (SMG:Trp63+Acta2+). This analysis of basal cells, paired with regional profiling, highlighted a unique expression profile of Keratins and established 21 total expressed genes along the proximal-distal and basal-luminal axes. Most notably, previously identified “stress-induced” Keratins, KRT6A and KRT17, were found to be present during homoeostasis, with expression in all basal cells as well as luminal squamous cells. Additionally, our luminal cell analysis identified three distinct types of club cells, with higher levels of Scgb1a1+ and Scgb3a2+ expression in more distal compared to proximal airway regions. We also identified mucous-producing cells in the larynx, distinguished by regional gene expression and distinct cellular morphology, specifically Il1a+ squamous cells and Kcnj16+ cuboidal cells. Lastly, we profiled diversity of SMG cells, highlighting Nkx3-1+ expression exclusive to mucous-1, mucous-2, and serous-acini cells, with ACTA2+ basal myoepithelial cells exhibiting a gene profile (Ntng1, Ntn4, Slit3, Ntrk3, Cxcl12, Cxcl14) linking potential neuroimmune strategies. Our single-cell transcriptomic analysis with in situ validations provides key insights into the transcriptomic and cellular heterogeneity of the upper airway and serves as a valuable atlas for hypothesis-driven work into responses to environmental insults, genetic mutations, and infectious diseases.

Results

Single cell transcriptomic analysis of the upper respiratory epithelium reveals compartmental and cellular heterogeneity

To explore epithelial cellular diversity of the upper respiratory epithelium, we started by using scRNA-seq to profile the pharyngolaryngeal-to-tracheobronchial axis. We isolated and pooled epithelial cells from pharynx to bronchi regions of adult wildtype C57BL/6 mice (n = 4 males, 4 females), and applied them to 10x Chromium for scRNA-seq (Figure 1A). Following quality control (Figure S1A), 3,617 epithelial single cells were recovered. Applying unsupervised clustering with Seurat default package,²⁵ followed by supervised annotation using known markers (Figures S1A–S1D),⁹^,¹²^,¹³^,¹⁶^,²⁶ we identified 18 cell types delineated with top markers (Figure 1B; Tables S1–S3). These 18 cell types segregate into three compartments; pharyngolaryngeal, tracheobronchial, and SMG (Figure 1C). In further analysis of the pharyngolaryngeal stratified squamous epithelium (SSE), we identified Tmprss11a as a marker for this compartment (Figure 1H). The tracheobronchial pseudostratified epithelium was marked by Nkx2-1, a known marker that was region-specific, aside for its expression in the thyroid epithelium (Figure 1I). In the SMG epithelia, we identified Dmbt1 as a region-specific marker, which was expressed at a high level with negligible expression in a few club cells in the proximal trachea and subglottic surface epithelium (Figure 1J). We validated these regional expression profiles using RNAscope on coronal sections along the pharyngolaryngeal-to-tracheobronchial axis, defining the spatial localization of the pharyngolaryngeal (Tmprss11+), tracheobronchial (Nkx2-1), and SMG (Dmbt1+) compartments (Figures 1D–1J; Figures S1E–S1G). Additionally, our analysis quantified the percentage of cells expressing each marker within their respective compartments, confirming enrichment of Tmprss11a expression in 66% of pharyngolaryngeal cells, Nkx2-1 in 64% of tracheobronchial cells, and Dmbt1 in 94% of SMG cells (Figure 1F; Figure S1G). These expression patterns elucidate the transcriptomic specificity for regional targeting of these epithelial compartments.

scRNA-seq of the upper respiratory epithelium reveal compartmental and cellular heterogeneity

(A) Schematic illustration of cell isolation and scRNA-seq workflow.

(B) Dotplot showing top markers for each cluster. Using default Seurat²⁵ dotplot settings (Methods), percentage expressed was plotted from 0% to 75% detected and the color bar shows the average of scaled normalized expression values across cells in a given cluster.

(C) UMAP plots of integrated upper airway scRNA-Seq data from adult WT B6 mice (n = 8 biological repeats, 4 male, 4 female).

(D and E) Schematic illustration of macro-anatomical regions of the upper airway with compartmental epithelial cell type diversity.

(F–J) Feature plots with RNAscope validation of *Tmprss11a* (pharyngolaryngeal) (H), *Nkx2-1* (tracheobronchial) (I), *Dmbt1* (seromucous glands) (J) exhibiting pan-epithelial regional markers in coronal sections. (G) H&E-stained upper airway. DAPI is in blue.

All images 20× magnification. Scale bar represents 500 μm.

H&E, hematoxylin and eosin; AC, arytenoid cartilage; CC, cricoid cartilage; TC, tracheal cartilage; SSE, stratified squamous epithelium; SG, subglottis; SMG, submucosal gland.

Genome-wide profiling identifies signatures of the stratified squamous epithelium (SSE) along the basal-luminal axis

The pharynx and larynx are mostly lined with SSE, which caudally extends to the infraglottic vocal fold (VF),²⁷^,²⁸ and is also present in hillock regions of the trachea.²⁰ This contrasts from the subglottis, trachea, and bronchi, which are lined with pseudostratified epithelium (Figures 1D, 1E, and 1G).²⁷^,²⁸ The multi-layered SSE serves to protect the underlying tissues from mechanical and chemical damage, pathogens, and dehydration.²⁰^,²⁹ Given the unique architecture, we hypothesized that this specialized SSE tissue would exhibit a basal-to-luminal coding pattern. Using SSE of the larynx as an example, we identified four cell clusters representing cell types along the basal-luminal axis: basal, parabasal, suprabasal/luminal, and mucous (Figures 2A and 2B). To explore the regional expression profile in situ, we utilized RNAscope and discovered that Igfbp2 is exclusively expressed in the basal layer, Tmprss11a expressed in the basal and parabasal layers, and KRT13 protein distribution extending from basal to suprabasal with increased expression to differentiated luminal cells (Figures 2B and 2C; Figure S1I). We also identified populations of mucous-producing cells dispersed throughout the pharyngolaryngeal apical epithelium, exhibiting top marker Il1a expression (Figure 1B; Figure S2C). Together, these data define the compartmental specificity of squamous epithelia and unique expression profiles during terminal differentiation.

Genome-wide profiling revealed signatures of stratified squamous epithelium along basal-luminal axis with extensive Keratin diversity along proximal-distal axis

(A) Integrated UMAP plot highlighting laryngeal surface epithelial cells.

(B and C) Feature plots with RNAscope validation of basal-to-luminal canonical markers for laryngeal SSE.

(D) Schematic illustration of KRT diversity along the proximal-to-distal upper respiratory axis.

(E) Schematic illustration of cranial view of larynx to display coronal sections (red/yellow dashed lines) attained for analysis.

(F and F′) Immunofluorescent coronal serial sections with insets of dorsal upper airway epithelium displaying regional KRT expression along various epithelium subtypes. Red line indicates transition zone from SSE to pseudostratified.

(G and H) Diversity of KRT expression to SSE of tracheal hillocks and laryngeal VF tissue.

DAPI is in blue. All images 20× magnification. Scale bar represents 100 μm (C, G, and H) and 500 μm (F).

Epi, epiglottis; AE, aryepiglottic fold; SG, subglottis; AC, arytenoid cartilage; CC, cricoid cartilage; TC, tracheal cartilage; SSE, stratified squamous epithelium; KRT, keratin; SMG, submucosal gland.

Systematic profiling of Keratin code along the proximal-distal and basal-luminal axes

Keratins are essential for maintaining the structure and function of epithelial cells, playing pivotal roles in homeostasis, tissue repair, and disease pathogenesis.³⁰^,³¹^,³²^,³³^,³⁴^,³⁵^,³⁶^,³⁷^,³⁸ Specific Keratins are often markers for specialized epithelial cells, e.g., KRT13+KRT14+ for VF SSE and KRT4+KRT13+ for tracheal hillocks.⁸^,²⁰^,³⁹ However, the cell type-specific expression and spatial pattern of diverse Keratins have not been systematically characterized in the upper respiratory epithelium. We started with exploring Keratin gene expression in our scRNA-seq dataset. Among the 63 known Keratin genes, expression of 21 of them were detected in the upper respiratory epithelium (Figure S3). Along the proximal-distal axis, we categorized the “Keratin code” based on scRNA-seq data (Figure 2D). We validated the data using immunofluorescence staining of key Keratins on serial sections (Figures 2E–2H; Figures S1H and S1I). The proximal-distal axis along the surface epithelium was defined by complementary KRT8 and KRT14 expression (Figure 2F; Figure S1H). Specifically, KRT8 was primarily detected in tracheal pseudostratified epithelium, SMG epithelium, and thyroid gland epithelium, while KRT14 was detected primarily in laryngeal SSE, myoepithelial cells of SMG, and a subset of basal cells of the proximal trachea (Figures 2F–2H; Figure S1H). KRT6A and KRT17, known as “stress-induced” Keratins,³⁰^,³¹^,³²^,³⁴ were both highly expressed in all three compartments, defined by exclusive basal cell expression in pseudostratified and SMG epithelium, albeit, enriched in both basal and luminal cells in laryngeal SSE and tracheal hillocks (Figures 2F–2H; Figure S1H). Interestingly, and unique to the mouse, the lingual side of the aryepiglottic fold, which transitions ventrally into the epiglottis, contains cornified epithelium which did not express KRT17 (Figures 2D and 2F; Figure S1H). KRT4 and KRT5 were expressed across all layers of the SSE; however, KRT5 expression was particularly enriched within basal cells (Figure S1I). We also compared the Keratin code in the VF and tracheal hillock squamous epithelium, two upper respiratory regions that are under constant mechanical insults. Keratin expression was abundant in both of these tissues with only subtle differences. Tracheal hillocks exhibited a Keratin coding pattern defined by basal markers (KRT4+KRT5+KRT6A+KRT14+KRT17+) and luminal markers (KRT4+KRT6A+KRT8+KRT13+) (Figures 2F and 2G). VF SSE displayed identical basal marker Keratin expression to hillocks, and with identical luminal markers excluding KRT8 to the VF medial edge (Figures 2F, 2G, and 2H). Together, these data reveal a complex Keratin code in the upper respiratory epithelium (Figure 2D; Figure S3).

Basal cells exhibit transcriptomic regional diversity with unique functional signatures

Basal cells pave the entire upper respiratory epithelium, from pharynx to bronchi marked by high expression of the putative progenitor cell marker Trp63 (Figure 3A–3C). To investigate the distinct patterning of these cells, we performed immunofluorescence and assessed Trp63 expression in three compartments. Our analysis revealed multi-layered TRP63+ basal cells in the SSE of the larynx, single-layered basal cells in the trachea and subglottis and distributed basal cells in the SMG (Figures 3B and 3B′). To further explore functional differences based on these anatomic locations, we isolated out basal populations in silico (Figure 3D) and performed differential expression (DE) analysis (p_adj = <0.01, avg_logFC = 2.5), comparing across basal subtypes (Figure 3E), followed by Gene Ontogeny (GO) and irGSEA hallmark pathways analysis (Figures 3E–3H; Figure S2A). The irGSEA hallmark pathway analysis, based on DE genes across all basal groups, found unique profiles within the basal-larynx, basal-myoepithelial, and basal-tracheal cell populations. Basal-larynx cells were enriched for G2M-Checkpoint, MYC-Targets-v1/v2, and DNA-Repair (Krt13, Igfbp2, Top2a, and Mki67). Basal-myoepithelial cells were enriched for Epithelial-Mesenchymal-Transition and Apical-Surface (Ntng1, Sox9, Acta2, and Ntrk3). Lastly, basal-tracheal cells showed enrichment for Inflammatory-Response and TNFα-Signaling-via-NF-κB (Aqp5, Krt19, Scgb3a2, Cyp4b1, Lgr6) (Figures 3E and 3F). To rigorously assess these transcriptomic signatures, we conducted Gene Ontology (GO) enrichment and identified relative DE genes associated with KEGG pathway terms (Figures 3G and 3H; Figure S2A; Table S4). Basal-larynx cells showed top enriched pathways related to Cell Cycle, p53 Signaling Pathway and Cellular Senescence (Cdc20, Ccna2, Ccnb2, Ccnb1, Cdk1, and Bub1b). Basal-tracheal cells exhibited enrichment for Bile Secretion, N-glycan Biosynthesis, and Cortisol Synthesis and Secretion (Nceh1, Aqp4, Atp1b1, Mgat4a, Mgat3, and Pde8a). Meanwhile, basal-myoepithelial cells showed enrichment for Focal Adhesion, Axon Guidance, and PI3K-Akt Signaling Pathway (Ntng1, Epha4, Epha7, Sema6a, Cxcl12, Wnt5b, Ntn4, Slit3, Pak3, and Myl9) (Figures 3G and 3H). For a comprehensive list of GO enrichment and irGSEA hallmark pathways analysis of DE genes across all epithelial cell types refer to Table S3 and Figure S4, respectively. This analysis highlights the distinct transcriptional and functional specialization of basal cell populations across the upper respiratory epithelium, providing a foundation for understanding their unique roles in homeostasis, repair, and regional adaptation.

Basal cells are progenitor cells that are critical for repair and regeneration of the epithelium following injury by differentiating into specialized cell types needed for tissue integrity. To compare the potential differentiation pathways in each of the basal cell types, we performed single cell trajectory analysis (Monocle 3). In silico, we selected cell clusters to only include each basal cell population (i.e., basal-larynx, basal-trachea, and basal-myoepithelial) alongside the differentiated cell types in the corresponding region (Figures S5A–S5C). The basal-larynx trajectory revealed a direct lineage progression from basal to parabasal to suprabasal cells, subsequently leading to differentiated cell populations including mucous, ciliated, and club cell identities. The basal-trachea trajectory displayed a sequential progression through club-mid, club-proximal, and club-distal stages, followed by mucous-related lineages, with ciliated cells representing the most distal point in pseudotime. In comparison, basal-myoepithelial cells followed an SMG lineage trajectory progressing through mucous-2, serous-acini, serous-duct, mucous-1, ending in ciliated cells (Figures S5A–S5C). Our data highlights that basal cells, despite their morphological similarity, exhibit transcriptional heterogeneity primed for shared but also distinct roles across different epithelial compartments.

Submucosal gland cell heterogeneity as revealed at single cell resolution

We sought to explore the heterogeneity of the murine SMG compartment, which has largely been unexplored at the single-cell level.³ Our scRNA-seq revealed the expected rich diversity of differentiated cell types in the SMG, and also uncovered markers for each population (Figures 4A and 4B). In the mouse upper respiratory tract, SMG were found in the pharynx, rostral laryngeal side of the epiglottis, subglottis, and proximal ventral trachea (Figures 4C and 4D). Based on our scRNA-seq data, these SMG epithelial cells segregate into multiple populations, including mucous-1 (Muc5b, Tff2), mucous-2 (Lipf), serous-duct (Slc34a2), serous-acini (Bpifb5, Lipf), and basal-myoepithelial cells (Acta2, Trp63), each with a distinct transcriptional profile (Figures 4A and 4B). Sox9 transcripts were also abundant in all SMG cells, with additional expression in the tuft cell population. Interestingly, RNAscope validation showed that the transcription factor Nkx3-1 was expressed in mucous-1, mucous-2, and serous-acini cells (Figure 4E), suggesting a unique role in regulating mucous producing cell fate. ⍺SMA+TRP63+ basal-myoepithelial cells were found primarily in the distal acinar compartment and did not overlap with Nkx3-1 expression (Figure 4E). Notably, all SMG compartments, including the epiglottic, subglottic, hypopharyngeal, and tracheal regions, exhibited similar expression patterns for known cell types, suggesting a shared transcriptional signature and conserved functional role across these anatomically distinct glandular populations (Figure S6). However, further lineage-tracing studies would be needed to formally determine whether distinct progenitor pools contribute to regional SMG formation. Together, these data delineate both shared and distinct signatures of murine SMG cell types.

Luminal cell heterogeneity as revealed at single cell resolution

We further analyzed our scRNA-seq dataset to reveal the diversity of luminal cell populations along the pharyngolaryngeal-to-tracheobronchial axis (Figures 5A–5C). Luminal cell types located to laryngeal SSE include suprabasal cells (KRT13, KRT17), mucous-producing cells (Il1a, Kcnj16), and taste bud complexes which include innervated neuroendocrine (Ascl1), type II (GNAT3) and type III (SNAP25) taste cells (Figure 5C; Figures S2B and S2C). Solitary neuroendocrine cells were also found within the highly innervated subglottic epithelium and decreased in number along the proximal-distal axis toward the tracheal epithelium (Figure 5C; Figure S2B). Luminal cell types located in the trachea epithelium include club (SCGB1A1, SCGB3A2), mucous (AGR2), ciliated (A-TUB), solitary tuft (DCLK1, Chat), and clustered hillocks (KRT13, KRT17). Ciliated cells were found throughout the pseudostratified epithelium of the trachea and in focal regions within the laryngeal epithelium, such as the superior VF and rostral lingual side of the epiglottis (Figure S2C). Additionally, mucous-1 cells, identified by the classic goblet cell marker AGR2,⁴⁰ were detected in the tracheal surface epithelium by immunofluorescence (Figure 5C) and were notably enriched within the Dmbt1+ SMG compartment (Figure S2D). We found heterogeneity within club cells along the tracheobronchial axis which we termed club-proximal, club-mid and club-distal, with subtypes showing unique regional transcriptomic signatures and cell abundance (Figure 5A; Figures S2E–S2K).

Luminal cell heterogeneity along pharyngolaryngeal-to-tracheobronchial axis revealed at single cell resolution

(A) Integrated UMAP plot highlighting luminal epithelial cells.

(B) H&E-stained upper airway displaying anatomic regions of interest with dashed black boxes representing localized regions for club-proximal, club-mid, and club-distal immunofluorescent corresponding images.

(C) Immunofluorescence images exhibiting unique markers for luminal cell types of the upper airway.

DAPI is in blue. Images are either 20× or 40× magnification. Scale bar represents 100 μm (C) and 500 μm (B).

H&E, hematoxylin and eosin; AC, arytenoid cartilage; CC, cricoid cartilage; TC, tracheal cartilage; TZ, transition zone; NE, neuroendocrine; SSE, stratified squamous epithelium.

To directly compare DE genes, we took a similar approach as our basal analysis and isolated club cells in silico, and subclustered the population (Figure S2I; Table S5). Notably, distal club cells expressed the highest levels of Scgb1a1 and Scgb3a2 (Figure 1B; Figures S2E–S2K), which was validated by pairwise DE comparison analysis (p_adj = <0.05, LogFC = 1) between club-proximal and club-distal (Figure S2H; Table S5). Slc6a15 emerged as the top marker for club-proximal population across all cell types (Figure 1B), which also showed elevated expression of Cadm2 (Figure S2J; Table S1), a key defining marker expressed in laryngeal club cells.¹³ Immunofluorescence confirmed an increase in SCGB1A1+SCGB3A2+ club cell abundance to the distal trachea and extrapulmonary bronchi compared to proximal regions (Figure 5C; Figures S2F and S2G). GO enrichment and pathway analysis revealed the top KEGG terms for club-proximal cells, including Ras Signaling Pathway (Angpt1, Abl1, Gab2, and Gnb5), and Calcium Signaling Pathway (Chrm3, Gna14, Erbb4, and Tacr1). In contrast, club-mid cells showed enrichment for KEGG terms related to Inflammatory Mediator Regulation of TRP Channels (Camk2d, F2rl1, Calml3) and Mucin-Type O-Glycan Biosynthesis (Galntl6 and B4galt5). Finally, club-distal cells were enriched for KEGG terms linked to Ribosome function and Coronavirus Disease (Mrpl4, Rps26, Mrpl2, Rps19, Rps7, Rpl36al, Rplp0, and Rps12) (Figure S2K; Table S5). Together, our analysis suggests potential regional-specific roles for club cells, where club-proximal cells located in the highly innervated laryngeal subglottis may play a prominent role in xenobiotic detection, while club-mid and club-distal cells localized to the trachea are primarily involved in secretory/mucin pathways and metabolic processes.

Discussion

To date, research has established diverse heterogeneity across the cellular landscape of the unified respiratory epithelium,⁸^,¹³ offering an initial basis for its complexity. Building on these investigations focused on individual organs, here, we present a comprehensive analysis of the single cell landscape in the murine upper airway, encompassing the pharyngolaryngeal-to-tracheobronchial axis. We defined three spatially distinct tissue compartments Tmprss11a+ pharyngolaryngeal, Nkx2-1+ tracheobronchial, and Dmbt1+ SMG epithelium, comprised of 18 transcriptionally unique cell types. This comprehensive regional atlas provides a critical framework for future studies aimed at understanding cellular dynamics in airway health, disease progression, and repair mechanisms.

Our analysis revealed a robust population of TRP63+ basal cells, characterized by regional transcriptomic diversity and distinct functional signatures. Consistent with prior studies demonstrating basal cell subpopulations in dorsal versus ventral tracheal regions,¹² we identified a dorsal-enriched population (Trp63+Cav1+) and a ventral-enriched population (Trp63+Tgm2+) of tracheal progenitors. In the larynx, basal cells exhibited unique enrichment for Igfbp2, with a concomitant reduction in Tmprss11a expression among SSE progenies. While Tmprss11a has been implicated in epithelial integrity and protease activity,⁴¹^,⁴² its specific role in the pharyngolaryngeal compartment remains unclear, highlighting the need for future targeted work. Laryngeal SSE display higher cellular turnover compared to the predominantly quiescent pseudostratified epithelium.²⁰^,⁴³ Comparative analyses between basal cells from the larynx and trachea revealed elevated expression of proliferative markers in laryngeal basal cells suggestive of key roles in tissue repair, while tracheal basal cells enriched for markers associated with secretory function indicate their contribution to mucosal defense. These findings highlight distinct regional specialization of basal cell populations between the larynx and trachea, underscoring their functional divergence in maintaining epithelial integrity.

We also explored Keratin gene expression within our scRNA-seq dataset with the goal of establishing a comprehensive Keratin profile of the upper respiratory epithelium. Systematic profiling identified a total of 21 represented Keratin genes, delineating a distinct expression code along both the proximal-distal and basal-luminal axes. Among these, we focused on the “stress-induced” Keratins, KRT6A and KRT17. While previous studies have reported ectopic expression of KRT6A and KRT17 in epithelium following wound healing³⁴ or in the context of viral persistence,³¹^,³² our findings reveal widespread expression under homeostatic conditions. Specifically, KRT6A+KRT17+ epithelium was detected in basal cells of the trachea and SMG, as well as in both basal and luminal cells of the laryngopharynx and tracheal hillocks. We also observed restricted expression of KRT8 to pseudostratified surface epithelium and KRT14 to squamous surface epithelium of the pharyngolarynx, delineating a distinct proximal-distal axis of epithelial differentiation. KRT13 at the protein and gene level marked suprabasal (i.e., luminal) SSE, however, was also modestly expressed in our basal-larynx population. Recent work has reported KRT13 expression in basal cells of hillocks.²⁰ Immunofluorescent validation found hillocks expressed a near identical Keratin code as VF SSE; thus, we suspect that hillocks share a sufficiently similar transcriptome with differentiated SSE, leading them to be grouped into our suprabasal-larynx population. To date, no studies have investigated airway SSE of both the larynx and trachea within one dataset at single cell resolution. These findings highlight previously uncharacterized Keratin dynamics across epithelial subtypes, providing new insights into airway squamous epithelial differentiation and potential shared transcriptional profiles with specialized structures like hillocks.

Next, we sought to characterize the cellular diversity of an underexplored, yet essential tissue compartment, the SMG. SMG cells play critical roles in upper airway immune defense, actively secreting mucus and antimicrobial byproducts to defend against pathogens and irritants, and with unique basal-myoepithelial cells.¹⁵^,⁴⁴ We identified Nkx3-1 transcription factor, uniquely expressed in mucous-1, mucous-2, and serous-acini cells. Nkx3-1, widely recognized as a prostate epithelium-specific marker, plays a key role in the development of the testes and prostate and has been implicated in prostate cancer.⁴⁵^,⁴⁶ Given its cell-specific expression and well-established roles in development, we hypothesize that this transcription factor may influence the cell fate of mucin-producing cells in the SMG, potentially serving as an effective candidate for disease modeling. The subglottic epithelium shares a close anatomical and functional relationship with adjacent SMG. Notably, Acta2+ myoepithelial cells within SMG function as reserve stem cells capable of regenerating the airway surface epithelium.³^,⁴⁷^,⁴⁸ In salivary glands, myoepithelial cells have been identified as key sources of neurotrophin signaling following irradiation-induced damage.⁴⁹ When comparing basal-myoepithelial cells from the larynx and trachea, we observed increased expression of genes associated with axon guidance (Ntng1, Wnt5b, and Ntn4), cytokine signaling (Cxcl12 and Cxcl14), and neurotrophic receptors (Ntrk2 and Ntrk3). The Cxcl12-Cxcr4 axis is a well-characterized signaling pathway with pleiotropic roles in immune responses, viral infections, and cancer.⁵⁰^,⁵¹ Recent studies have further demonstrated that Cxcr4 is upregulated following influenza infection, modulating neutrophil phagocytic activity, ligand-specific migration, and the induction of neutrophil extracellular traps.⁵² Based on our findings, we propose that basal-myoepithelial cells may play a critical role in injury repair and immune response mediated by the Cxcl12/14-Cxcr4 axis. In addition, we detected upregulation of CHAT muscarinic receptors (Chrm1 and Chrm3) in basal-myoepithelial cells, suggesting potential crosstalk with cholinergic-expressing neurons in the airways. These findings highlight the intimate relationship between basal-myoepithelial cells and innervating neurons and suggest a critical role for these cells in mediating neuroimmune interactions that contribute to airway homeostasis and disease.

The upper respiratory epithelium of the larynx and trachea contains several shared luminal cell types, including club, mucous/goblet, and ciliated cells. However, the transcriptomic differences between these regions remain incompletely characterized. To address this, we hypothesized that distinct transcriptomic profiles would emerge based on anatomical localization. Our analysis identified three transcriptionally distinct populations of club cells, with higher levels of Scgb1a1+ and Scgb3a2+ expression in more distal compared to proximal regions along the upper airway axis. Club cells, defined by secretoglobin family genes (Scgb1a1, Scgb3a1, and Scgb3a2), have been shown to exhibit functional heterogeneity in previous studies.⁸^,⁵³^,⁵⁴ A unique club-proximal population, exclusively expressing the defining laryngeal club cell marker Cadm2, was localized to the larynx.¹³ Additional populations, designated as club-mid and club-distal, were restricted to the proximal trachea and distal trachea/extrapulmonary bronchi, respectively. Notably, laryngeal club cells, which have only recently been described, exhibit both squamous and cuboidal morphologies,²⁷ and are implicated in detoxification, repair, regeneration, and anti-inflammatory processes.¹³^,⁵⁴^,⁵⁵ We identified rare mucous-producing cell populations within the larynx, characterized by squamous or cuboidal morphology and elevated expression of mucin-related genes (Muc1, Muc4, Muc13, and Muc20). These cells were further distinguished by distinct top markers: Il1a for squamous-type and Kcnj16 for cuboidal-type populations. Notably, Il1a+ cells exhibited strong expression of Duoxa2, an enzyme essential for thyroid hormone synthesis and mucosal immunity.⁵⁶ Additional genes highly expressed in this rare cell subset included interleukin signaling components (Il1a, Il1r2, and Il1rn), the transcription factor Irf7, and genes related to inflammatory responses and pathogen defense (Isg15, Tnf, and Tnfrsf11b). Collectively, the unique transcriptional signature of these cells suggests a specialized role in coordinating mucosal defense and immune responses to pathogens within the laryngeal epithelium.

In addition to commonly shared cell types, several rare yet functionally significant populations exist in the upper respiratory tract, including neuroendocrine cells, tuft cells, and ionocytes, each contributing uniquely to airway homeostasis and function. Our dataset identified both neuroendocrine and tuft cells; however, ionocytes were not detected. The relatively modest cell yield (3,617 cells) likely limited our capacity to detect rare or transient cell populations, such as ionocytes, transitional epithelial cells, or cells sensitive to processing-related loss.⁸ Solitary neuroendocrine cells expressed canonical markers (Ascl1, Calca)⁸^,⁵⁷ and exhibited elevated expression of Cxcl13 and Ngf, with the highest abundance within the laryngeal subglottic epithelium. Neuroendocrine cells in the lung, known as specialized airway sensors, are critical for interacting with peripheral nerves to regulate immune responses to allergens.⁵⁸^,⁵⁹ Tuft cells in our dataset were marked by classical tuft cell genes (Pou2f3, Spib, Trpm5, and Dclk1) along with genes associated with immune regulation (Rgs13, Rgs14, Rgs21, Tnfrsf13b, and Il17rb),¹³ type II taste cell marker (GNAT3) and basal cell identity (Lgr5). Although limited cell numbers precluded identification of subpopulations, other studies have reported heterogeneity within tuft cells, including tuft-1 (Pou2f3) and tuft-2 (Pou2f3, Gfi1b, Spib, and Sox9) subtypes in the tracheal epithelium.⁸^,²¹ Tuft cells have also been characterized as sources of IL-25, prostaglandins, leukotrienes, and acetylcholine,¹⁹^,²²^,²³^,²⁴^,⁶⁰^,⁶¹ which are crucial for promoting innate type 2 immunity and protective neural reflexes.⁶² Our use of Chat^cre;tdTomato lineage-marked tissue confirmed CHAT expression in tracheal tuft cells, supporting their acetylcholine-producing capacity and aligning with previous work.¹⁸^,⁶³ While our in silico analysis indicated that both mucous-1 and mucous-2 cells localize predominantly to the Dmbt1-positive SMG compartment, our immunofluorescence data suggest that a subset of mucous-1 cells also resides in the tracheal surface epithelium, aligning with previous work.⁸ These findings underscore the extensive heterogeneity of epithelial cells across multiple upper airway compartments, emphasizing their roles in defending against environmental insults and maintaining homeostasis.

The extensive transcriptomic and cell diversity of the upper respiratory tract enables the epithelium to perform a range of functions, from forming physical barriers and initiating immune responses to repairing tissue damage and regulating inflammation. Our findings establish a region-specific specialization of epithelial diversity and a detailed cross-organ single-cell atlas along the pharyngolaryngeal-to-tracheobronchial axis, offering valuable insights into the diverse cell populations that maintain airway health and resilience.

Limitations of the study

While our single-cell transcriptomic atlas of larynx and trachea epithelium provides unprecedented insights into airway cellular heterogeneity, several constraints warrant acknowledgment. The limited biological replication (8 mice yielding 3,617 cells) potentially restricts our ability to capture rare cell populations, such as ionocytes, which typically constitute <1% of airway epithelium. Furthermore, our technical design impedes robust pseudo-bulk statistical comparisons, potentially affecting the confidence intervals of our differential expression analyses. Our focus on healthy adult murine tissue, while establishing a critical baseline, necessarily constrains immediate translation to developmental trajectories or pathological states. These limitations present clear opportunities for future research. Expanding the dataset with greater biological replication would enhance statistical power for detecting subtle transcriptional signatures. Additionally, extending this atlas to include developmental timepoints and disease models would significantly amplify its translational impact, particularly for respiratory conditions affecting the upper airways. Ultimately, this work serves as a foundational resource that invites expansion across physiological states to fully capitalize on its potential for understanding airway biology and pathology.

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Dr. Xin Sun (xinsun@health.ucsd.edu).

Materials availability

This study did not generate new unique reagents.

Data and code availability

Single cell RNA-seq data have been deposited at the Gene Expression Omnibus (GSE287495) and are publicly available as of the date of publication. All data generated supporting the findings of this study are available in the manuscript. Further information is available from the lead author upon reasonable request. This paper does not report original code.

Acknowledgments

The authors thank Sun lab members for discussions. UCSD Microscopy Core was supported by NINDS-P30NS047101. This work was supported by grants NIH NHLBI R01 AT011676-01 (to X.S.), 1R01 HL160019-01 (to X.S.), and NIH NIDCD F32 DC021634-01 (to A.G.F.).

Author contributions

Conceptualization: A.G.F. and X.S.; methodology: A.G.F.; investigation: A.G.F.; visualization: A.G.F.; funding acquisition: A.G.F. and X.S.; supervision: X.S.; writing – original draft: A.G.F.; writing – review and editing: A.G.F. and X.S.

Declaration of interests

The authors declare no competing interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies

Rabbit, anti-KRT13	Abcam	Cat#ab92551; RRID: AB_2134681
Mouse, anti-KRT14	Millipore	Cat#CBL197; RRID: AB_2132747
Rabbit, anti-KRT17	Santa Cruz Biotechnology	Cat#sc-393002; RRID: AB_2893006
Rabbit-KRT6a	LS Bio	Cat#LS-B12036
Rabbit-KRT8	LS Bio	Cat#LS-B7928
Rabbit-KRT4	ThermoFisher	Cat#MA5-37810; RRID: AB_2897734
Rabbit-KRT5	Abcam	Cat#ab52635; RRID: AB_869890
Mouse-aSMA-FITC	Millipore Sigma	Cat#F3777; RRID: AB_476977
Mouse, anti-P63	Biomedical	Cat#CM-163-A; RRID: AB_10582730
Rabbit-MUC5B	Cloud-Clone Corp	Cat#PAA684Mu01
Rabbit-SNAP25	Sigma-Aldrich	Cat#S9684; RRID: AB_261576
Goat-GNAT3 (a-gustducin)	Aviva Systems Biology	Cat#OEAB00418
Mouse-TUJ1	Novus	Cat#BAM1195; RRID: AB_456859
Rabbit-AGR2	Cell Signaling Technology	Cat#13062S
Rabbit-Dclk1	Abcam	Cat#ab37991
Mouse-RFP	Abcam	Cat#ab125244; RRID: AB_10973556
Mouse-FOXJ1	eBioscience/Fisher	Cat#5013931
Mouse-Acetylated tubulin	Sigma	Cat#T7451; RRID: AB_609894
Rabbit-SCGB1A1	Abcam	Cat#ab213203; RRID: AB_2650558
Rat- SCGB3A2	R&D Systems	Cat#381707
Goat anti-Rabbit IgG Alexa Flour 488	ThermoFisher	Cat#A27034; RRID: AB_2536097
Goat anti-mouse IgG, Cy3^TM	Jackson Immuno Research	Cat#115-166-003; RRID: AB_2338699
Goat anti-rat IgG, Cy3^TM	Jackson Immuno Research	Cat#112-165-003; RRID: AB_2338240
BV510-conjugated anti-CD45	BioLegend	Cat#103138; RRID: AB_2563061
APC-FITC-conjugated anti-Epcam	BioLegend	Cat#118214; RRID: AB_1134102
PE-conjugated anti-CD31	BioLegend	Cat#102508; RRID: AB_312915
CD11b: Brilliant Violet 450	BioLegend	Cat#75-0112-U025; RRID: AB_2621936
Mm-Tmprss11a	Advanced Cell Diagnostics	Cat#1232761
Mm-Nkx2.1	Advanced Cell Diagnostics	Cat#434721
Mm-Nkx3.1	Advanced Cell Diagnostics	Cat#472111
Mm-Kcnj16	Advanced Cell Diagnostics	Cat#492481
Mm-Il1a	Advanced Cell Diagnostics	Cat#440398
Mm-Dmbt1	Advanced Cell Diagnostics	Cat#418561
Mm-Igfbp2	Advanced Cell Diagnostics	Cat#405951
Mm-Lpo	Advanced Cell Diagnostics	Cat#403521
Mm-Slc34a2	Advanced Cell Diagnostics	Cat#449311

Chemicals, peptides, and recombinant proteins

Tamoxifen	Sigma-Aldrich	Cat#T-5648-1G
RPMI 1640 Medium	Thermo Scientific	Cat#11875093
1 mM HEPES	Life Technology	Cat#15630080
Dispase	Roche	Cat#4942086001
DNase I	Roche	Cat#11284932001
1 mM CaCl2	Sigma-Aldrich	Cat#21115
1 mM MgCl2	Sigma-Aldrich	Cat#20303M
Collagenase D	Roche	Cat#11088858001
RBC Lysis Buffer	BioLegend	Cat#420301

Critical commercial assays

RNAscope® Multiplex Fluorescent Reagent Kit V2	Advanced Cell Diagnostics	Cat#323100

Deposited data

Single cell RNA-seq data for mouse larynx/trachea	Gene Expression Omnibus	GSE287495

Experimental models: Organisms/strains

Mouse: Wildtype C57BL/6J	Jackson laboratory	JAX: 000664
Mouse: Chat-cre	Jackson laboratory	JAX: 031661
Mouse: Rosa-lxl-tdTomato (Ai14)	Jackson laboratory	JAX: 007914
Mouse: Ascl1-creERT2	Jackson laboratory	JAX: 012882

Software and algorithms

RStudio (v2024.12.1 + 563)	RStudio Team	http://www.rstudio.com/
R (v4.5.0)	R Core Team	https://www.R-project.org/
Cell Ranger (v3.0.2)	10x Genomics	https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger

Open in a new tab

Experimental model and study participant details

All experimental procedures were performed in the American Association for Accreditation of Laboratory Animal Care (AAALAC)-certified laboratory animal facility at the University of California, San Diego (UCSD). All animal husbandry and experiments were conducted under approved Institutional Animal Care and Use Committee (IACUC) guidelines. Wild-type C57BL/6 (JAX 000664), Ascl1^CreERT2 (JAX 012882), Rosa^lxl-tdTomato (Ai14, JAX 007914), and Chat^cre (JAX 031661) lines were purchased from the Jackson lab. All the cre lines we used in this study were kept in C57BL/6 background. All cre driver lines in heterozygous form are viable and fertile, and no abnormal phenotypes were detected. Both male and female mice were used in the experiment. Adult mice were 8–10 weeks of age for all experiments.

Method details

Tissue collection and immunofluorescence staining

Mice were euthanized by CO2 inhalation followed by transcardial perfusion with PBS to remove circulating blood. The larynx-trachea was isolated and fixed overnight in 1%PFA or immediately embedded in OCT, flash frozen using 2-Methylbutane and liquid nitrogen and stored at −80°C. Using a cryostat, the larynx-trachea was then sectioned (12 μm) and stored at −20°C. Unfixed tissue was then washed in PBS for 5 min to remove OCT, heated to boiling in 10 mM citrate buffer (pH 9) for antigen retrieval and treated with 0.5% Triton X-100 in PBS for 15min. All sections were processed for immunostaining following a standard protocol.²⁷ Ascl1 recombination was induced via Tamoxifen IP administration with dose of 100 mg/kg for three consecutive days. All primary and secondary antibodies used are listed in Table S6. Primary antibodies were applied overnight at 4°C, while secondary antibodies were applied for 1h at room temperature. Sections were incubated with DAPI (1:1,000 ratio) for 10 min at RT. Slides were mounted and coverslipped with Prolong Diamond mounting media (Fisher P36970), cured flat at room temperature in the dark for 24 h, and stored at 4°C. Each experiment was replicated at least twice for all timepoints and targets assessed.

RNAscope in situ hybridization

All staining procedures were performed using the RNAscope Fluorescent Multiplex Kit V2 (Advanced Cell Diagnostics, no. 323100) following the manufacturer’s instructions. The following probes from Advanced Cell Diagnostics were used: Mm-Tmprss11a (no. 1232761), Mm-Nkx2.1 (no. 434721), Mm-Nkx3.1 (no. 472111), Mm-Dmbt1 (no. 418561), Mm-Igfbp2 (no. 405951), Mm-Lpo (no. 403521), Mm-Slc34a2 (no. 449311), Mm-Il1a (no. 440398), Mm-Kcnj16 (no. 492481).

Tissue processing and flow cytometry

Tissue spanning from the lower pharynx and larynx, beginning at the base of the tongue and tip of the epiglottis and extending distally to the tracheobronchial carina (larynx-trachea), was mechanically dissociated by finely mincing with razor blades in solution containing 5 mL of RPMI 1640 (Thermo Scientific) with 10% fetal bovine serum, 1 mM HEPES (Life Technology), 1 mM MgCl₂ (Life Technology), 1 mM CaCl₂ (Sigma), 0.5 mg mL⁻¹ collagenase D and type I/dispase (Roche), and 0.25 mg DNase I (Roche). Minced tissue was then digested by shaking at around 150 rpm for 30 min at 37°C. Following incubation, upper airway pieces were mechanically dissociated further by straining through a MACS 70 μm filter. Red blood cells were removed by the addition of 1 mL of RBC lysis buffer (BioLegend) to each tube and incubation at room temperature for 1 min. Single-cell suspensions were pelleted (1,500 rpm, 4°C, 5 min), counted with a hemocytometer and diluted to around 1 × 10⁶ cells ml⁻¹. Diluted cells were stained with Fc blocking antibody (5 mg mL⁻¹, BD). To isolate epithelial lineage, cells were then incubated with the following specific surface marker antibodies: (Immune) 1:1,000 BV510-conjugated anti-CD45 (BioLegend, no. 103138), (Epithelial) 1:1,000 APC-FITC-conjugated anti-Epcam (BioLegend, no. 118214), and (Endothelial) 1:1,000 PE-conjugated anti-CD31 (BioLegend, no. 102508). Cells were then stained using live/dead dye (CD11b: Brilliant Violet 450, BioLegend, cat#75-0112-U025) before being resuspended in 2% FBS +1:2,000 DAPI. All FACS sorting was done on a BD FACSAria Fusion Sorter (BD Biosciences) analyzer with three lasers (405, 488 and 640 nm) at the Flow Cytometry Core at VA San Diego Health Care System and San Diego Veterans Medical Research Foundation. All data were further analyzed and plotted with FlowJo software (Tree Star).

Quantification and statistical analysis

Data quality control, normalization, variable genes selection, principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and differential gene expression testing were conducted using the Seurat (v4.0.5) pipeline.⁶⁴^,⁶⁵

Preprocessing of 10X chromium scRNAseq data

Single cells were processed into complementary DNA libraries using Chromium Single Cell 3′ v3 kit (10X Genomics, Pleasanton, CA) and sequencing was carried out on the NovaSeq (Illumina) platform. The CellRanger software package from 10X Genomics (v3.0.2) was used to align raw reads onto the mouse reference genome (GRCm38) and generate single-cell gene barcode matrices. CellBender (v0.3.0)⁶⁶ was then used to remove technical artifacts and ambient RNA to produce improved estimates of gene expression. To ensure the exclusion of potential thyroid epithelial contamination, we manually removed the thyroid gland during sample preparation and computationally filtered clusters expressing thyroid-specific genes (Folr1, Tg, Foxe1) from our dataset. Single cells below 15% mitochondrial reads and between 200 and 7,500 unique genes were considered high-quality cells and were filtered for further analyses. Additional filtering included a minimum UMI cutoff of 2 and minimum cell count cutoff of 10. LogNormalize global-scaling normalization method was applied with regressing variables cell cycle, S.Score, G2M.Score, and mitochondrial RNA level to remove confounding effect on downstream clustering. In addition, DoubletFinder (v2.0)⁶⁷ was used to remove doublets and LogNormalize was again used to normalize feature expression.

Dimensionality reduction using PCA and UMAP

Selection of variable genes was implemented with the function FindVariableFeatures with method = “vst.” The top 3,000 highly variable genes were centered and scaled with function ScaleData. The first 50 PCs were calculated via function RunPCA, after which the function RunUMAP was applied to generate UMAP embeddings for 2-dimensional visualization.

Integration, clustering, and downstream analysis

CCA⁶⁸ was used to integrate dataset across two experimental batches for a total of n = 8 mice (4 males, 4 females). In total 3,617 epithelial single cells were recovered for analysis. To determine dimensions for optimized clustering, we evaluated the optimal cutoff using an elbow plot and settled on using the first 50 principal components for clustering and projection with UMAP. Clustering resolutions was set to 0.8 (Seurat default), resulting in 19 interim clusters representing potential unique cell populations. Following manual inspection of top markers, we combined several clusters with shared markers to ensure that annotated clusters would show unique transcriptional profiles, resulting in 18 distinct clusters. For rigorous definition of marker genes in each cluster, we performed unsupervised and supervised analysis to screen each cluster’s top marker genes (FindAllMarkers) using ViolinPlot, FeaturePlot and DotPlot. To complement the default FindAllMarkers “Wilcoxon rank-sum test,” we utilized a “Model-based analysis of single-cell transcriptomics (MAST) test” that is particularly well-suited for single-cell data because it uses a hurdle model to account for both the dropout rate (the probability that a gene is not detected in a cell) and the continuous expression level among cells that do express the gene. Cell clusters were referenced against available literature.⁸^,¹³^,¹⁶ Cell clusters were validated with RNAscope and immunofluorescence staining (see main figures). We present a list of the top 200 differentially expressed marker genes for all cell clusters, analyzed using both Wilcoxon and MAST tests, in Table S3. Using FDR-corrected Fisher’s combined p-value <0.001 and a minimum avg_log2FC of 2.0, we identified the top 100 genes per cluster, listed in Table S1. High-throughput sequence data is available at GEO. Once exclusively differentially expressed genes were identified, we performed tests of enrichment using Gene Ontology (GO) annotations utilizing Enrichr (v2024)⁶⁹ as previously described.⁷⁰

Published: June 7, 2025

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2025.112845.

Supplemental information

Document S1. Figures S1–S6

mmc1.pdf^{(19.8MB, pdf)}

Table S1. Consensus marker genes for cell-type clusters from 3′ droplet-based scRNA-seq data (3,617 cells)

mmc2.xlsx^{(26.4KB, xlsx)}

Table S2. Cell-type enriched TFs, GPCRs and KRTs from 3′ droplet-based scRNA-seq data (3,617 cells)

mmc3.xlsx^{(16.7KB, xlsx)}

Table S3. Differentially expressed genes (top n = 200) across all cells identified using Seurat’s FindAllMarkers with both Wilcoxon rank-sum and MAST tests

mmc4.xlsx^{(648.7KB, xlsx)}

Table S4. Table of significantly differentially expressed marker genes comparing basal-larynx versus basal-trachea versus basal-myoepithelial populations

mmc5.xlsx^{(2.2MB, xlsx)}

Table S5. Table of significantly differentially expressed marker genes comparing club-proximal versus club-distal populations

mmc6.xlsx^{(757.3KB, xlsx)}

References

1.Hogan B.L.M., Barkauskas C.E., Chapman H.A., Epstein J.A., Jain R., Hsia C.C.W., Niklason L., Calle E., Le A., Randell S.H., et al. Repair and regeneration of the respiratory system: complexity, plasticity, and mechanisms of lung stem cell function. Cell Stem Cell. 2014;15:123–138. doi: 10.1016/j.stem.2014.07.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Beppu A.K., Zhao J., Yao C., Carraro G., Israely E., Coelho A.L., Drake K., Hogaboam C.M., Parks W.C., Kolls J.K., Stripp B.R. Epithelial plasticity and innate immune activation promote lung tissue remodeling following respiratory viral infection. Nat. Commun. 2023;14:5814. doi: 10.1038/s41467-023-41387-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Tata A., Kobayashi Y., Chow R.D., Tran J., Desai A., Massri A.J., McCord T.J., Gunn M.D., Tata P.R. Myoepithelial cells of submucosal glands can function as reserve stem cells to regenerate airways after injury. Cell Stem Cell. 2018;22:668–683.e6. doi: 10.1016/j.stem.2018.03.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Hewitt R.J., Lloyd C.M. Regulation of immune responses by the airway epithelial cell landscape. Nat. Rev. Immunol. 2021;21:347–362. doi: 10.1038/s41577-020-00477-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Carlier F.M., de Fays C., Pilette C. Epithelial barrier dysfunction in chronic respiratory diseases. Front. Physiol. 2021;12 doi: 10.3389/fphys.2021.691227. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Drake M.G., Cook M., Fryer A.D., Jacoby D.B., Scott G.D. Airway sensory nerve plasticity in asthma and chronic cough. Front. Physiol. 2021;12 doi: 10.3389/fphys.2021.720538. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Dworkin J.P. Laryngitis: types, causes, and treatments. Otolaryngol. Clin. North Am. 2008;41:419–436. doi: 10.1016/j.otc.2007.11.011. ix. [DOI] [PubMed] [Google Scholar]
8.Montoro D.T., Haber A.L., Biton M., Vinarsky V., Lin B., Birket S.E., Yuan F., Chen S., Leung H.M., Villoria J., et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature. 2018;560:319–324. doi: 10.1038/s41586-018-0393-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Deprez M., Zaragosi L.E., Truchi M., Becavin C., Ruiz García S., Arguel M.J., Plaisant M., Magnone V., Lebrigand K., Abelanet S., et al. A single-cell atlas of the human healthy airways. Am. J. Respir. Crit. Care Med. 2020;202:1636–1645. doi: 10.1164/rccm.201911-2199OC. [DOI] [PubMed] [Google Scholar]
10.Engler A.E., Ysasi A.B., Pihl R.M.F., Villacorta-Martin C., Heston H.M., Richardson H.M.K., Thapa B.R., Moniz N.R., Belkina A.C., Mazzilli S.A., Rock J.R. Airway-associated macrophages in homeostasis and repair. Cell Rep. 2020;33 doi: 10.1016/j.celrep.2020.108553. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Plasschaert L.W., Žilionis R., Choo-Wing R., Savova V., Knehr J., Roma G., Klein A.M., Jaffe A.B. A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte. Nature. 2018;560:377–381. doi: 10.1038/s41586-018-0394-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Zhou Y., Yang Y., Guo L., Qian J., Ge J., Sinner D., Ding H., Califano A., Cardoso W.V. Airway basal cells show regionally distinct potential to undergo metaplastic differentiation. eLife. 2022;11 doi: 10.7554/eLife.80083. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.An R., Ni Z., Xie E., Rey F.E., Kendziorski C., Thibeault S.L. Single-cell view into the role of microbiota shaping host immunity in the Larynx. iScience. 2024;27 doi: 10.1016/j.isci.2024.110156. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Lynch T.J., Anderson P.J., Rotti P.G., Tyler S.R., Crooke A.K., Choi S.H., Montoro D.T., Silverman C.L., Shahin W., Zhao R., et al. Submucosal gland myoepithelial cells are reserve stem cells that can regenerate mouse tracheal epithelium. Cell Stem Cell. 2018;22:779. doi: 10.1016/j.stem.2018.04.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Ostedgaard L.S., Price M.P., Whitworth K.M., Abou Alaiwa M.H., Fischer A.J., Warrier A., Samuel M., Spate L.D., Allen P.D., Hilkin B.M., et al. Lack of airway submucosal glands impairs respiratory host defenses. eLife. 2020;9 doi: 10.7554/eLife.59653. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Yu W., Moninger T.O., Thurman A.L., Xie Y., Jain A., Zarei K., Powers L.S., Pezzulo A.A., Stoltz D.A., Welsh M.J. Cellular and molecular architecture of submucosal glands in wild-type and cystic fibrosis pigs. Proc. Natl. Acad. Sci. USA. 2022;119 doi: 10.1073/pnas.2119759119. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Seeholzer L.F., Julius D. Neuroendocrine cells initiate protective upper airway reflexes. Science. 2024;384:295–301. doi: 10.1126/science.adh5483. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Billipp T.E., Fung C., Webeck L.M., Sargent D.B., Gologorsky M.B., Chen Z., McDaniel M.M., Kasal D.N., McGinty J.W., Barrow K.A., et al. Tuft cell-derived acetylcholine promotes epithelial chloride secretion and intestinal helminth clearance. Immunity (Camb., Mass.) 2024;57:1243–1259.e8. doi: 10.1016/j.immuni.2024.03.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Saunders C.J., Reynolds S.D., Finger T.E. Chemosensory brush cells of the trachea. A stable population in a dynamic epithelium. Am. J. Respir. Cell Mol. Biol. 2013;49:190–196. doi: 10.1165/rcmb.2012-0485OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Lin B., Shah V.S., Chernoff C., Sun J., Shipkovenska G.G., Vinarsky V., Waghray A., Xu J., Leduc A.D., Hintschich C.A., et al. Airway hillocks are injury-resistant reservoirs of unique plastic stem cells. Nature. 2024;629:869–877. doi: 10.1038/s41586-024-07377-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.O'Leary C.E., Schneider C., Locksley R.M. Tuft cells-systemically dispersed sensory epithelia integrating immune and neural circuitry. Annu. Rev. Immunol. 2019;37:47–72. doi: 10.1146/annurev-immunol-042718-041505. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Hollenhorst M.I., Jurastow I., Nandigama R., Appenzeller S., Li L., Vogel J., Wiederhold S., Althaus M., Empting M., Altmüller J., et al. Tracheal brush cells release acetylcholine in response to bitter tastants for paracrine and autocrine signaling. FASEB J. 2020;34:316–332. doi: 10.1096/fj.201901314RR. [DOI] [PubMed] [Google Scholar]
23.Ualiyeva S., Hallen N., Kanaoka Y., Ledderose C., Matsumoto I., Junger W.G., Barrett N.A., Bankova L.G. Airway brush cells generate cysteinyl leukotrienes through the ATP sensor P2Y2. Sci. Immunol. 2020;5 doi: 10.1126/sciimmunol.aax7224. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Perniss A., Boonen B., Tonack S., Thiel M., Poharkar K., Alnouri M.W., Keshavarz M., Papadakis T., Wiegand S., Pfeil U., et al. A succinate/SUCNR1-brush cell defense program in the tracheal epithelium. Sci. Adv. 2023;9 doi: 10.1126/sciadv.adg8842. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Hao Y., Hao S., Andersen-Nissen E., Mauck W.M., Zheng S., Butler A., Lee M.J., Wilk A.J., Darby C., Zager M., et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184:3573–3587.e29. doi: 10.1016/j.cell.2021.04.048. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Madissoon E., Oliver A.J., Kleshchevnikov V., Wilbrey-Clark A., Polanski K., Richoz N., Ribeiro Orsi A., Mamanova L., Bolt L., Elmentaite R., et al. A spatially resolved atlas of the human lung characterizes a gland-associated immune niche. Nat. Genet. 2023;55:66–77. doi: 10.1038/s41588-022-01243-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Foote A.G., Lungova V., Thibeault S.L. Piezo1-expressing vocal fold epithelia modulate remodeling via effects on self-renewal and cytokeratin differentiation. Cell. Mol. Life Sci. 2022;79:591. doi: 10.1007/s00018-022-04622-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Foote A.G., Tibbetts J., Bartley S.M., Thibeault S.L. Localization of TRPV3/4 and PIEZO1/2 sensory receptors in murine and human larynges. Laryngoscope Investig. Otolaryngol. 2022;7:1963–1972. doi: 10.1002/lio2.968. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Levendoski E.E., Leydon C., Thibeault S.L. Vocal fold epithelial barrier in health and injury: a research review. J. Speech Lang. Hear. Res. 2014;57:1679–1691. doi: 10.1044/2014_JSLHR-S-13-0283. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Wang W., Lozar T., Golfinos A.E., Lee D., Gronski E., Ward-Shaw E., Hayes M., Bruce J.Y., Kimple R.J., Hu R., et al. Stress keratin 17 expression in head and neck cancer contributes to immune evasion and resistance to immune-checkpoint blockade. Clin. Cancer Res. 2022;28:2953–2968. doi: 10.1158/1078-0432.CCR-21-3039. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Wang W., Uberoi A., Spurgeon M., Gronski E., Majerciak V., Lobanov A., Hayes M., Loke A., Zheng Z.M., Lambert P.F. Stress keratin 17 enhances papillomavirus infection-induced disease by downregulating T cell recruitment. PLoS Pathog. 2020;16 doi: 10.1371/journal.ppat.1008206. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Wang W., Spurgeon M.E., Pope A., McGregor S., Ward-Shaw E., Gronski E., Lambert P.F. Stress keratin 17 and estrogen support viral persistence and modulate the immune environment during cervicovaginal murine papillomavirus infection. Proc. Natl. Acad. Sci. USA. 2023;120 doi: 10.1073/pnas.2214225120. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Simonson L., Vold S., Mowers C., Massey R.J., Ong I.M., Longley B.J., Chang H. Keratin 13 deficiency causes white sponge nevus in mice. Dev. Biol. 2020;468:146–153. doi: 10.1016/j.ydbio.2020.07.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Zhang X., Yin M., Zhang L.J. Keratin 6, 16 and 17-critical barrier alarmin molecules in skin wounds and psoriasis. Cells. 2019;8 doi: 10.3390/cells8080807. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Regenbogen E., Mo M., Romeiser J., Shroyer A.L.W., Escobar-Hoyos L.F., Burke S., Shroyer K.R. Elevated expression of keratin 17 in oropharyngeal squamous cell carcinoma is associated with decreased survival. Head Neck. 2018;40:1788–1798. doi: 10.1002/hed.25164. [DOI] [PubMed] [Google Scholar]
36.Cohen-Kerem R., Madah W., Sabo E., Rahat M.A., Greenberg E., Elmalah I. Cytokeratin-17 as a potential marker for squamous cell carcinoma of the larynx. Ann. Otol. Rhinol. Laryngol. 2004;113:821–827. doi: 10.1177/000348940411301008. [DOI] [PubMed] [Google Scholar]
37.Mikami T., Cheng J., Maruyama S., Kobayashi T., Funayama A., Yamazaki M., Adeola H.A., Wu L., Shingaki S., Saito C., Saku T. Emergence of keratin 17 vs. loss of keratin 13: their reciprocal immunohistochemical profiles in oral carcinoma in situ. Oral Oncol. 2011;47:497–503. doi: 10.1016/j.oraloncology.2011.03.015. [DOI] [PubMed] [Google Scholar]
38.Sakamoto K., Aragaki T., Morita K.I., Kawachi H., Kayamori K., Nakanishi S., Omura K., Miki Y., Okada N., Katsube K.I., et al. Down-regulation of keratin 4 and keratin 13 expression in oral squamous cell carcinoma and epithelial dysplasia: a clue for histopathogenesis. Histopathology (Oxf.) 2011;58:531–542. doi: 10.1111/j.1365-2559.2011.03759.x. [DOI] [PubMed] [Google Scholar]
39.Lungova V., Verheyden J.M., Herriges J., Sun X., Thibeault S.L. Ontogeny of the mouse vocal fold epithelium. Dev. Biol. 2015;399:263–282. doi: 10.1016/j.ydbio.2014.12.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Al-Shaibi A.A., Abdel-Motal U.M., Hubrack S.Z., Bullock A.N., Al-Marri A.A., Agrebi N., Al-Subaiey A.A., Ibrahim N.A., Charles A.K., COLORS in IBD-Qatar Study Group, et al. Human AGR2 deficiency causes mucus barrier dysfunction and infantile inflammatory bowel disease. Cell. Mol. Gastroenterol. Hepatol. 2021;12:1809–1830. doi: 10.1016/j.jcmgh.2021.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Zhang C., Zhang Y., Zhang S., Wang Z., Sun S., Liu M., Chen Y., Dong N., Wu Q. Intracellular autoactivation of TMPRSS11A, an airway epithelial transmembrane serine protease. J. Biol. Chem. 2020;295:12686–12696. doi: 10.1074/jbc.RA120.014525. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Zmora P., Hoffmann M., Kollmus H., Moldenhauer A.S., Danov O., Braun A., Winkler M., Schughart K., Pöhlmann S. TMPRSS11A activates the influenza A virus hemagglutinin and the MERS coronavirus spike protein and is insensitive against blockade by HAI-1. J. Biol. Chem. 2018;293:13863–13873. doi: 10.1074/jbc.RA118.001273. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Watson J.K., Rulands S., Wilkinson A.C., Wuidart A., Ousset M., Van Keymeulen A., Göttgens B., Blanpain C., Simons B.D., Rawlins E.L. Clonal dynamics reveal two distinct populations of basal cells in slow-turnover airway epithelium. Cell Rep. 2015;12:90–101. doi: 10.1016/j.celrep.2015.06.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Kutta H., Steven P., Tillmann B.N., Tsokos M., Paulsen F.P. Region-specific immunological response of the different laryngeal compartments: significance of larynx-associated lymphoid tissue. Cell Tissue Res. 2003;311:365–371. doi: 10.1007/s00441-002-0692-y. [DOI] [PubMed] [Google Scholar]
45.Antao A.M., Ramakrishna S., Kim K.S. The role of Nkx3.1 in cancers and stemness. Int. J. Stem Cells. 2021;14:168–179. doi: 10.15283/ijsc20121. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Anderson P.D., McKissic S.A., Logan M., Roh M., Franco O.E., Wang J., Doubinskaia I., van der Meer R., Hayward S.W., Eischen C.M., et al. Nkx3.1 and Myc crossregulate shared target genes in mouse and human prostate tumorigenesis. J. Clin. Investig. 2012;122:1907–1919. doi: 10.1172/JCI58540. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Hegab A.E., Nickerson D.W., Ha V.L., Darmawan D.O., Gomperts B.N. Repair and regeneration of tracheal surface epithelium and submucosal glands in a mouse model of hypoxic-ischemic injury. Respirology (Carlton South) 2012;17:1101–1113. doi: 10.1111/j.1440-1843.2012.02204.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Carraro G., Stripp B.R. Roles for myoepithelial cells in the formation and maintenance of submucosal glands. Am. J. Respir. Cell Mol. Biol. 2017;56:685–686. doi: 10.1165/rcmb.2017-0044ED. [DOI] [PubMed] [Google Scholar]
49.Chibly A.M., Patel V.N., Aure M.H., Pasquale M.C., NIDCD/NIDCR Genomics and Computational Biology Core, Martin G.E., Ghannam M., Andrade J., Denegre N.G., Simpson C., et al. Neurotrophin signaling is a central mechanism of salivary dysfunction after irradiation that disrupts myoepithelial cells. NPJ Regen. Med. 2023;8 doi: 10.1038/s41536-023-00290-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Hara T., Tanegashima K. CXCL14 antagonizes the CXCL12-CXCR4 signaling axis. Biomol. Concepts. 2014;5:167–173. doi: 10.1515/bmc-2014-0007. [DOI] [PubMed] [Google Scholar]
51.Pozzobon T., Goldoni G., Viola A., Molon B. CXCR4 signaling in health and disease. Immunol. Lett. 2016;177:6–15. doi: 10.1016/j.imlet.2016.06.006. [DOI] [PubMed] [Google Scholar]
52.Rudd J.M., Pulavendran S., Ashar H.K., Ritchey J.W., Snider T.A., Malayer J.R., Marie M., Chow V.T.K., Narasaraju T. Neutrophils induce a novel chemokine receptors repertoire during influenza pneumonia. Front. Cell. Infect. Microbiol. 2019;9:108. doi: 10.3389/fcimb.2019.00108. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Kathiriya J.J., Brumwell A.N., Jackson J.R., Tang X., Chapman H.A. Distinct airway epithelial stem cells hide among club cells but mobilize to promote alveolar regeneration. Cell Stem Cell. 2020;26:346–358.e4. doi: 10.1016/j.stem.2019.12.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Reynolds S.D., Reynolds P.R., Pryhuber G.S., Finder J.D., Stripp B.R. Secretoglobins SCGB3A1 and SCGB3A2 define secretory cell subsets in mouse and human airways. Am. J. Respir. Crit. Care Med. 2002;166:1498–1509. doi: 10.1164/rccm.200204-285OC. [DOI] [PubMed] [Google Scholar]
55.Xu M., Yang W., Wang X., Nayak D.K. Lung secretoglobin Scgb1a1 influences alveolar macrophage-mediated inflammation and immunity. Front. Immunol. 2020;11 doi: 10.3389/fimmu.2020.584310. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.De Deken X., Corvilain B., Dumont J.E., Miot F. Roles of DUOX-mediated hydrogen peroxide in metabolism, host defense, and signaling. Antioxid. Redox Signal. 2014;20:2776–2793. doi: 10.1089/ars.2013.5602. [DOI] [PubMed] [Google Scholar]
57.Kuo C.S., Darmanis S., Diaz de Arce A., Liu Y., Almanzar N., Wu T.T.H., Quake S.R., Krasnow M.A. Neuroendocrinology of the lung revealed by single-cell RNA sequencing. eLife. 2022;11 doi: 10.7554/eLife.78216. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Branchfield K., Nantie L., Verheyden J.M., Sui P., Wienhold M.D., Sun X. Pulmonary neuroendocrine cells function as airway sensors to control lung immune response. Science. 2016;351:707–710. doi: 10.1126/science.aad7969. [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Sui P., Wiesner D.L., Xu J., Zhang Y., Lee J., Van Dyken S., Lashua A., Yu C., Klein B.S., Locksley R.M., et al. Pulmonary neuroendocrine cells amplify allergic asthma responses. Science. 2018;360 doi: 10.1126/science.aan8546. [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Pan J., Zhang L., Shao X., Huang J. Acetylcholine from tuft cells: the updated insights beyond its immune and chemosensory functions. Front. Cell Dev. Biol. 2020;8:606. doi: 10.3389/fcell.2020.00606. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Keshavarz M., Faraj Tabrizi S., Ruppert A.L., Pfeil U., Schreiber Y., Klein J., Brandenburger I., Lochnit G., Bhushan S., Perniss A., et al. Cysteinyl leukotrienes and acetylcholine are biliary tuft cell cotransmitters. Sci. Immunol. 2022;7 doi: 10.1126/sciimmunol.abf6734. [DOI] [PubMed] [Google Scholar]
62.Barr J., Gentile M.E., Lee S., Kotas M.E., Fernanda de Mello Costa M., Holcomb N.P., Jaquish A., Palashikar G., Soewignjo M., McDaniel M., et al. Injury-induced pulmonary tuft cells are heterogenous, arise independent of key Type 2 cytokines, and are dispensable for dysplastic repair. eLife. 2022;11 doi: 10.7554/eLife.78074. [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Nadjsombati M.S., McGinty J.W., Lyons-Cohen M.R., Jaffe J.B., DiPeso L., Schneider C., Miller C.N., Pollack J.L., Nagana Gowda G.A., Fontana M.F., et al. Detection of succinate by intestinal tuft cells triggers a type 2 innate immune circuit. Immunity (Camb., Mass.) 2018;49:33–41.e7. doi: 10.1016/j.immuni.2018.06.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Satija R., Farrell J.A., Gennert D., Schier A.F., Regev A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 2015;33:495–502. doi: 10.1038/nbt.3192. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Becht E., McInnes L., Healy J., Dutertre C.A., Kwok I.W.H., Ng L.G., Ginhoux F., Newell E.W. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 2018;37:38–44. doi: 10.1038/nbt.4314. [DOI] [PubMed] [Google Scholar]
66.Fleming S.J., Chaffin M.D., Arduini A., Akkad A.-D., Banks E., Marioni J.C., Philippakis A.A., Ellinor P.T., Babadi M. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat. Methods. 2023;20:1323–1335. doi: 10.1038/s41592-023-01943-7. [DOI] [PubMed] [Google Scholar]
67.McGinnis C.S., Murrow L.M., Gartner Z.J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 2019;8:329–337.e4. doi: 10.1016/j.cels.2019.03.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
68.González I., Déjean S., Martin P.G., Baccini A. CCA: An R package to extend canonical correlation analysis. J. Stat. Software. 2008;23:1–14. [Google Scholar]
69.Xie Z., Bailey A., Kuleshov M.V., Clarke D.J.B., Evangelista J.E., Jenkins S.L., Lachmann A., Wojciechowicz M.L., Kropiwnicki E., Jagodnik K.M., et al. Gene Set Knowledge Discovery with Enrichr. Curr. Protoc. 2021;1 doi: 10.1002/cpz1.90. [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Foote A.G., Wang Z., Kendziorski C., Thibeault S.L. Tissue specific human fibroblast differential expression based on RNAsequencing analysis. BMC Genom. 2019;20:308. doi: 10.1186/s12864-019-5682-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1–S6

mmc1.pdf^{(19.8MB, pdf)}

Table S1. Consensus marker genes for cell-type clusters from 3′ droplet-based scRNA-seq data (3,617 cells)

mmc2.xlsx^{(26.4KB, xlsx)}

Table S2. Cell-type enriched TFs, GPCRs and KRTs from 3′ droplet-based scRNA-seq data (3,617 cells)

mmc3.xlsx^{(16.7KB, xlsx)}

Table S3. Differentially expressed genes (top n = 200) across all cells identified using Seurat’s FindAllMarkers with both Wilcoxon rank-sum and MAST tests

mmc4.xlsx^{(648.7KB, xlsx)}

Table S4. Table of significantly differentially expressed marker genes comparing basal-larynx versus basal-trachea versus basal-myoepithelial populations

mmc5.xlsx^{(2.2MB, xlsx)}

Table S5. Table of significantly differentially expressed marker genes comparing club-proximal versus club-distal populations

mmc6.xlsx^{(757.3KB, xlsx)}

Data Availability Statement

[bib1] 1.Hogan B.L.M., Barkauskas C.E., Chapman H.A., Epstein J.A., Jain R., Hsia C.C.W., Niklason L., Calle E., Le A., Randell S.H., et al. Repair and regeneration of the respiratory system: complexity, plasticity, and mechanisms of lung stem cell function. Cell Stem Cell. 2014;15:123–138. doi: 10.1016/j.stem.2014.07.012. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib2] 2.Beppu A.K., Zhao J., Yao C., Carraro G., Israely E., Coelho A.L., Drake K., Hogaboam C.M., Parks W.C., Kolls J.K., Stripp B.R. Epithelial plasticity and innate immune activation promote lung tissue remodeling following respiratory viral infection. Nat. Commun. 2023;14:5814. doi: 10.1038/s41467-023-41387-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib3] 3.Tata A., Kobayashi Y., Chow R.D., Tran J., Desai A., Massri A.J., McCord T.J., Gunn M.D., Tata P.R. Myoepithelial cells of submucosal glands can function as reserve stem cells to regenerate airways after injury. Cell Stem Cell. 2018;22:668–683.e6. doi: 10.1016/j.stem.2018.03.018. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib4] 4.Hewitt R.J., Lloyd C.M. Regulation of immune responses by the airway epithelial cell landscape. Nat. Rev. Immunol. 2021;21:347–362. doi: 10.1038/s41577-020-00477-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib5] 5.Carlier F.M., de Fays C., Pilette C. Epithelial barrier dysfunction in chronic respiratory diseases. Front. Physiol. 2021;12 doi: 10.3389/fphys.2021.691227. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib6] 6.Drake M.G., Cook M., Fryer A.D., Jacoby D.B., Scott G.D. Airway sensory nerve plasticity in asthma and chronic cough. Front. Physiol. 2021;12 doi: 10.3389/fphys.2021.720538. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib7] 7.Dworkin J.P. Laryngitis: types, causes, and treatments. Otolaryngol. Clin. North Am. 2008;41:419–436. doi: 10.1016/j.otc.2007.11.011. ix. [DOI] [PubMed] [Google Scholar]

[bib8] 8.Montoro D.T., Haber A.L., Biton M., Vinarsky V., Lin B., Birket S.E., Yuan F., Chen S., Leung H.M., Villoria J., et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature. 2018;560:319–324. doi: 10.1038/s41586-018-0393-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib9] 9.Deprez M., Zaragosi L.E., Truchi M., Becavin C., Ruiz García S., Arguel M.J., Plaisant M., Magnone V., Lebrigand K., Abelanet S., et al. A single-cell atlas of the human healthy airways. Am. J. Respir. Crit. Care Med. 2020;202:1636–1645. doi: 10.1164/rccm.201911-2199OC. [DOI] [PubMed] [Google Scholar]

[bib10] 10.Engler A.E., Ysasi A.B., Pihl R.M.F., Villacorta-Martin C., Heston H.M., Richardson H.M.K., Thapa B.R., Moniz N.R., Belkina A.C., Mazzilli S.A., Rock J.R. Airway-associated macrophages in homeostasis and repair. Cell Rep. 2020;33 doi: 10.1016/j.celrep.2020.108553. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib11] 11.Plasschaert L.W., Žilionis R., Choo-Wing R., Savova V., Knehr J., Roma G., Klein A.M., Jaffe A.B. A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte. Nature. 2018;560:377–381. doi: 10.1038/s41586-018-0394-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib12] 12.Zhou Y., Yang Y., Guo L., Qian J., Ge J., Sinner D., Ding H., Califano A., Cardoso W.V. Airway basal cells show regionally distinct potential to undergo metaplastic differentiation. eLife. 2022;11 doi: 10.7554/eLife.80083. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib13] 13.An R., Ni Z., Xie E., Rey F.E., Kendziorski C., Thibeault S.L. Single-cell view into the role of microbiota shaping host immunity in the Larynx. iScience. 2024;27 doi: 10.1016/j.isci.2024.110156. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib14] 14.Lynch T.J., Anderson P.J., Rotti P.G., Tyler S.R., Crooke A.K., Choi S.H., Montoro D.T., Silverman C.L., Shahin W., Zhao R., et al. Submucosal gland myoepithelial cells are reserve stem cells that can regenerate mouse tracheal epithelium. Cell Stem Cell. 2018;22:779. doi: 10.1016/j.stem.2018.04.007. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib15] 15.Ostedgaard L.S., Price M.P., Whitworth K.M., Abou Alaiwa M.H., Fischer A.J., Warrier A., Samuel M., Spate L.D., Allen P.D., Hilkin B.M., et al. Lack of airway submucosal glands impairs respiratory host defenses. eLife. 2020;9 doi: 10.7554/eLife.59653. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib16] 16.Yu W., Moninger T.O., Thurman A.L., Xie Y., Jain A., Zarei K., Powers L.S., Pezzulo A.A., Stoltz D.A., Welsh M.J. Cellular and molecular architecture of submucosal glands in wild-type and cystic fibrosis pigs. Proc. Natl. Acad. Sci. USA. 2022;119 doi: 10.1073/pnas.2119759119. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib17] 17.Seeholzer L.F., Julius D. Neuroendocrine cells initiate protective upper airway reflexes. Science. 2024;384:295–301. doi: 10.1126/science.adh5483. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib18] 18.Billipp T.E., Fung C., Webeck L.M., Sargent D.B., Gologorsky M.B., Chen Z., McDaniel M.M., Kasal D.N., McGinty J.W., Barrow K.A., et al. Tuft cell-derived acetylcholine promotes epithelial chloride secretion and intestinal helminth clearance. Immunity (Camb., Mass.) 2024;57:1243–1259.e8. doi: 10.1016/j.immuni.2024.03.023. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib19] 19.Saunders C.J., Reynolds S.D., Finger T.E. Chemosensory brush cells of the trachea. A stable population in a dynamic epithelium. Am. J. Respir. Cell Mol. Biol. 2013;49:190–196. doi: 10.1165/rcmb.2012-0485OC. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib20] 20.Lin B., Shah V.S., Chernoff C., Sun J., Shipkovenska G.G., Vinarsky V., Waghray A., Xu J., Leduc A.D., Hintschich C.A., et al. Airway hillocks are injury-resistant reservoirs of unique plastic stem cells. Nature. 2024;629:869–877. doi: 10.1038/s41586-024-07377-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib21] 21.O'Leary C.E., Schneider C., Locksley R.M. Tuft cells-systemically dispersed sensory epithelia integrating immune and neural circuitry. Annu. Rev. Immunol. 2019;37:47–72. doi: 10.1146/annurev-immunol-042718-041505. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib22] 22.Hollenhorst M.I., Jurastow I., Nandigama R., Appenzeller S., Li L., Vogel J., Wiederhold S., Althaus M., Empting M., Altmüller J., et al. Tracheal brush cells release acetylcholine in response to bitter tastants for paracrine and autocrine signaling. FASEB J. 2020;34:316–332. doi: 10.1096/fj.201901314RR. [DOI] [PubMed] [Google Scholar]

[bib23] 23.Ualiyeva S., Hallen N., Kanaoka Y., Ledderose C., Matsumoto I., Junger W.G., Barrett N.A., Bankova L.G. Airway brush cells generate cysteinyl leukotrienes through the ATP sensor P2Y2. Sci. Immunol. 2020;5 doi: 10.1126/sciimmunol.aax7224. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib24] 24.Perniss A., Boonen B., Tonack S., Thiel M., Poharkar K., Alnouri M.W., Keshavarz M., Papadakis T., Wiegand S., Pfeil U., et al. A succinate/SUCNR1-brush cell defense program in the tracheal epithelium. Sci. Adv. 2023;9 doi: 10.1126/sciadv.adg8842. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib25] 25.Hao Y., Hao S., Andersen-Nissen E., Mauck W.M., Zheng S., Butler A., Lee M.J., Wilk A.J., Darby C., Zager M., et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184:3573–3587.e29. doi: 10.1016/j.cell.2021.04.048. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib26] 26.Madissoon E., Oliver A.J., Kleshchevnikov V., Wilbrey-Clark A., Polanski K., Richoz N., Ribeiro Orsi A., Mamanova L., Bolt L., Elmentaite R., et al. A spatially resolved atlas of the human lung characterizes a gland-associated immune niche. Nat. Genet. 2023;55:66–77. doi: 10.1038/s41588-022-01243-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib27] 27.Foote A.G., Lungova V., Thibeault S.L. Piezo1-expressing vocal fold epithelia modulate remodeling via effects on self-renewal and cytokeratin differentiation. Cell. Mol. Life Sci. 2022;79:591. doi: 10.1007/s00018-022-04622-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib28] 28.Foote A.G., Tibbetts J., Bartley S.M., Thibeault S.L. Localization of TRPV3/4 and PIEZO1/2 sensory receptors in murine and human larynges. Laryngoscope Investig. Otolaryngol. 2022;7:1963–1972. doi: 10.1002/lio2.968. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib29] 29.Levendoski E.E., Leydon C., Thibeault S.L. Vocal fold epithelial barrier in health and injury: a research review. J. Speech Lang. Hear. Res. 2014;57:1679–1691. doi: 10.1044/2014_JSLHR-S-13-0283. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib30] 30.Wang W., Lozar T., Golfinos A.E., Lee D., Gronski E., Ward-Shaw E., Hayes M., Bruce J.Y., Kimple R.J., Hu R., et al. Stress keratin 17 expression in head and neck cancer contributes to immune evasion and resistance to immune-checkpoint blockade. Clin. Cancer Res. 2022;28:2953–2968. doi: 10.1158/1078-0432.CCR-21-3039. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib31] 31.Wang W., Uberoi A., Spurgeon M., Gronski E., Majerciak V., Lobanov A., Hayes M., Loke A., Zheng Z.M., Lambert P.F. Stress keratin 17 enhances papillomavirus infection-induced disease by downregulating T cell recruitment. PLoS Pathog. 2020;16 doi: 10.1371/journal.ppat.1008206. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib32] 32.Wang W., Spurgeon M.E., Pope A., McGregor S., Ward-Shaw E., Gronski E., Lambert P.F. Stress keratin 17 and estrogen support viral persistence and modulate the immune environment during cervicovaginal murine papillomavirus infection. Proc. Natl. Acad. Sci. USA. 2023;120 doi: 10.1073/pnas.2214225120. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib33] 33.Simonson L., Vold S., Mowers C., Massey R.J., Ong I.M., Longley B.J., Chang H. Keratin 13 deficiency causes white sponge nevus in mice. Dev. Biol. 2020;468:146–153. doi: 10.1016/j.ydbio.2020.07.016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib34] 34.Zhang X., Yin M., Zhang L.J. Keratin 6, 16 and 17-critical barrier alarmin molecules in skin wounds and psoriasis. Cells. 2019;8 doi: 10.3390/cells8080807. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib35] 35.Regenbogen E., Mo M., Romeiser J., Shroyer A.L.W., Escobar-Hoyos L.F., Burke S., Shroyer K.R. Elevated expression of keratin 17 in oropharyngeal squamous cell carcinoma is associated with decreased survival. Head Neck. 2018;40:1788–1798. doi: 10.1002/hed.25164. [DOI] [PubMed] [Google Scholar]

[bib36] 36.Cohen-Kerem R., Madah W., Sabo E., Rahat M.A., Greenberg E., Elmalah I. Cytokeratin-17 as a potential marker for squamous cell carcinoma of the larynx. Ann. Otol. Rhinol. Laryngol. 2004;113:821–827. doi: 10.1177/000348940411301008. [DOI] [PubMed] [Google Scholar]

[bib37] 37.Mikami T., Cheng J., Maruyama S., Kobayashi T., Funayama A., Yamazaki M., Adeola H.A., Wu L., Shingaki S., Saito C., Saku T. Emergence of keratin 17 vs. loss of keratin 13: their reciprocal immunohistochemical profiles in oral carcinoma in situ. Oral Oncol. 2011;47:497–503. doi: 10.1016/j.oraloncology.2011.03.015. [DOI] [PubMed] [Google Scholar]

[bib38] 38.Sakamoto K., Aragaki T., Morita K.I., Kawachi H., Kayamori K., Nakanishi S., Omura K., Miki Y., Okada N., Katsube K.I., et al. Down-regulation of keratin 4 and keratin 13 expression in oral squamous cell carcinoma and epithelial dysplasia: a clue for histopathogenesis. Histopathology (Oxf.) 2011;58:531–542. doi: 10.1111/j.1365-2559.2011.03759.x. [DOI] [PubMed] [Google Scholar]

[bib39] 39.Lungova V., Verheyden J.M., Herriges J., Sun X., Thibeault S.L. Ontogeny of the mouse vocal fold epithelium. Dev. Biol. 2015;399:263–282. doi: 10.1016/j.ydbio.2014.12.037. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib40] 40.Al-Shaibi A.A., Abdel-Motal U.M., Hubrack S.Z., Bullock A.N., Al-Marri A.A., Agrebi N., Al-Subaiey A.A., Ibrahim N.A., Charles A.K., COLORS in IBD-Qatar Study Group, et al. Human AGR2 deficiency causes mucus barrier dysfunction and infantile inflammatory bowel disease. Cell. Mol. Gastroenterol. Hepatol. 2021;12:1809–1830. doi: 10.1016/j.jcmgh.2021.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib41] 41.Zhang C., Zhang Y., Zhang S., Wang Z., Sun S., Liu M., Chen Y., Dong N., Wu Q. Intracellular autoactivation of TMPRSS11A, an airway epithelial transmembrane serine protease. J. Biol. Chem. 2020;295:12686–12696. doi: 10.1074/jbc.RA120.014525. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib42] 42.Zmora P., Hoffmann M., Kollmus H., Moldenhauer A.S., Danov O., Braun A., Winkler M., Schughart K., Pöhlmann S. TMPRSS11A activates the influenza A virus hemagglutinin and the MERS coronavirus spike protein and is insensitive against blockade by HAI-1. J. Biol. Chem. 2018;293:13863–13873. doi: 10.1074/jbc.RA118.001273. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib43] 43.Watson J.K., Rulands S., Wilkinson A.C., Wuidart A., Ousset M., Van Keymeulen A., Göttgens B., Blanpain C., Simons B.D., Rawlins E.L. Clonal dynamics reveal two distinct populations of basal cells in slow-turnover airway epithelium. Cell Rep. 2015;12:90–101. doi: 10.1016/j.celrep.2015.06.011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib44] 44.Kutta H., Steven P., Tillmann B.N., Tsokos M., Paulsen F.P. Region-specific immunological response of the different laryngeal compartments: significance of larynx-associated lymphoid tissue. Cell Tissue Res. 2003;311:365–371. doi: 10.1007/s00441-002-0692-y. [DOI] [PubMed] [Google Scholar]

[bib45] 45.Antao A.M., Ramakrishna S., Kim K.S. The role of Nkx3.1 in cancers and stemness. Int. J. Stem Cells. 2021;14:168–179. doi: 10.15283/ijsc20121. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib46] 46.Anderson P.D., McKissic S.A., Logan M., Roh M., Franco O.E., Wang J., Doubinskaia I., van der Meer R., Hayward S.W., Eischen C.M., et al. Nkx3.1 and Myc crossregulate shared target genes in mouse and human prostate tumorigenesis. J. Clin. Investig. 2012;122:1907–1919. doi: 10.1172/JCI58540. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib47] 47.Hegab A.E., Nickerson D.W., Ha V.L., Darmawan D.O., Gomperts B.N. Repair and regeneration of tracheal surface epithelium and submucosal glands in a mouse model of hypoxic-ischemic injury. Respirology (Carlton South) 2012;17:1101–1113. doi: 10.1111/j.1440-1843.2012.02204.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib48] 48.Carraro G., Stripp B.R. Roles for myoepithelial cells in the formation and maintenance of submucosal glands. Am. J. Respir. Cell Mol. Biol. 2017;56:685–686. doi: 10.1165/rcmb.2017-0044ED. [DOI] [PubMed] [Google Scholar]

[bib49] 49.Chibly A.M., Patel V.N., Aure M.H., Pasquale M.C., NIDCD/NIDCR Genomics and Computational Biology Core, Martin G.E., Ghannam M., Andrade J., Denegre N.G., Simpson C., et al. Neurotrophin signaling is a central mechanism of salivary dysfunction after irradiation that disrupts myoepithelial cells. NPJ Regen. Med. 2023;8 doi: 10.1038/s41536-023-00290-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib50] 50.Hara T., Tanegashima K. CXCL14 antagonizes the CXCL12-CXCR4 signaling axis. Biomol. Concepts. 2014;5:167–173. doi: 10.1515/bmc-2014-0007. [DOI] [PubMed] [Google Scholar]

[bib51] 51.Pozzobon T., Goldoni G., Viola A., Molon B. CXCR4 signaling in health and disease. Immunol. Lett. 2016;177:6–15. doi: 10.1016/j.imlet.2016.06.006. [DOI] [PubMed] [Google Scholar]

[bib52] 52.Rudd J.M., Pulavendran S., Ashar H.K., Ritchey J.W., Snider T.A., Malayer J.R., Marie M., Chow V.T.K., Narasaraju T. Neutrophils induce a novel chemokine receptors repertoire during influenza pneumonia. Front. Cell. Infect. Microbiol. 2019;9:108. doi: 10.3389/fcimb.2019.00108. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib53] 53.Kathiriya J.J., Brumwell A.N., Jackson J.R., Tang X., Chapman H.A. Distinct airway epithelial stem cells hide among club cells but mobilize to promote alveolar regeneration. Cell Stem Cell. 2020;26:346–358.e4. doi: 10.1016/j.stem.2019.12.014. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib54] 54.Reynolds S.D., Reynolds P.R., Pryhuber G.S., Finder J.D., Stripp B.R. Secretoglobins SCGB3A1 and SCGB3A2 define secretory cell subsets in mouse and human airways. Am. J. Respir. Crit. Care Med. 2002;166:1498–1509. doi: 10.1164/rccm.200204-285OC. [DOI] [PubMed] [Google Scholar]

[bib55] 55.Xu M., Yang W., Wang X., Nayak D.K. Lung secretoglobin Scgb1a1 influences alveolar macrophage-mediated inflammation and immunity. Front. Immunol. 2020;11 doi: 10.3389/fimmu.2020.584310. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib56] 56.De Deken X., Corvilain B., Dumont J.E., Miot F. Roles of DUOX-mediated hydrogen peroxide in metabolism, host defense, and signaling. Antioxid. Redox Signal. 2014;20:2776–2793. doi: 10.1089/ars.2013.5602. [DOI] [PubMed] [Google Scholar]

[bib57] 57.Kuo C.S., Darmanis S., Diaz de Arce A., Liu Y., Almanzar N., Wu T.T.H., Quake S.R., Krasnow M.A. Neuroendocrinology of the lung revealed by single-cell RNA sequencing. eLife. 2022;11 doi: 10.7554/eLife.78216. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib58] 58.Branchfield K., Nantie L., Verheyden J.M., Sui P., Wienhold M.D., Sun X. Pulmonary neuroendocrine cells function as airway sensors to control lung immune response. Science. 2016;351:707–710. doi: 10.1126/science.aad7969. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib59] 59.Sui P., Wiesner D.L., Xu J., Zhang Y., Lee J., Van Dyken S., Lashua A., Yu C., Klein B.S., Locksley R.M., et al. Pulmonary neuroendocrine cells amplify allergic asthma responses. Science. 2018;360 doi: 10.1126/science.aan8546. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib60] 60.Pan J., Zhang L., Shao X., Huang J. Acetylcholine from tuft cells: the updated insights beyond its immune and chemosensory functions. Front. Cell Dev. Biol. 2020;8:606. doi: 10.3389/fcell.2020.00606. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib61] 61.Keshavarz M., Faraj Tabrizi S., Ruppert A.L., Pfeil U., Schreiber Y., Klein J., Brandenburger I., Lochnit G., Bhushan S., Perniss A., et al. Cysteinyl leukotrienes and acetylcholine are biliary tuft cell cotransmitters. Sci. Immunol. 2022;7 doi: 10.1126/sciimmunol.abf6734. [DOI] [PubMed] [Google Scholar]

[bib62] 62.Barr J., Gentile M.E., Lee S., Kotas M.E., Fernanda de Mello Costa M., Holcomb N.P., Jaquish A., Palashikar G., Soewignjo M., McDaniel M., et al. Injury-induced pulmonary tuft cells are heterogenous, arise independent of key Type 2 cytokines, and are dispensable for dysplastic repair. eLife. 2022;11 doi: 10.7554/eLife.78074. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib63] 63.Nadjsombati M.S., McGinty J.W., Lyons-Cohen M.R., Jaffe J.B., DiPeso L., Schneider C., Miller C.N., Pollack J.L., Nagana Gowda G.A., Fontana M.F., et al. Detection of succinate by intestinal tuft cells triggers a type 2 innate immune circuit. Immunity (Camb., Mass.) 2018;49:33–41.e7. doi: 10.1016/j.immuni.2018.06.016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib64] 64.Satija R., Farrell J.A., Gennert D., Schier A.F., Regev A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 2015;33:495–502. doi: 10.1038/nbt.3192. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib65] 65.Becht E., McInnes L., Healy J., Dutertre C.A., Kwok I.W.H., Ng L.G., Ginhoux F., Newell E.W. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 2018;37:38–44. doi: 10.1038/nbt.4314. [DOI] [PubMed] [Google Scholar]

[bib66] 66.Fleming S.J., Chaffin M.D., Arduini A., Akkad A.-D., Banks E., Marioni J.C., Philippakis A.A., Ellinor P.T., Babadi M. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat. Methods. 2023;20:1323–1335. doi: 10.1038/s41592-023-01943-7. [DOI] [PubMed] [Google Scholar]

[bib67] 67.McGinnis C.S., Murrow L.M., Gartner Z.J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 2019;8:329–337.e4. doi: 10.1016/j.cels.2019.03.003. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib68] 68.González I., Déjean S., Martin P.G., Baccini A. CCA: An R package to extend canonical correlation analysis. J. Stat. Software. 2008;23:1–14. [Google Scholar]

[bib69] 69.Xie Z., Bailey A., Kuleshov M.V., Clarke D.J.B., Evangelista J.E., Jenkins S.L., Lachmann A., Wojciechowicz M.L., Kropiwnicki E., Jagodnik K.M., et al. Gene Set Knowledge Discovery with Enrichr. Curr. Protoc. 2021;1 doi: 10.1002/cpz1.90. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib70] 70.Foote A.G., Wang Z., Kendziorski C., Thibeault S.L. Tissue specific human fibroblast differential expression based on RNAsequencing analysis. BMC Genom. 2019;20:308. doi: 10.1186/s12864-019-5682-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Cellular heterogeneity and patterning strategies as revealed by upper respiratory epithelium single cell atlas

Alexander G Foote

Xin Sun

Summary

Graphical abstract

Highlights

Introduction

Results

Single cell transcriptomic analysis of the upper respiratory epithelium reveals compartmental and cellular heterogeneity

Figure 1.

Genome-wide profiling identifies signatures of the stratified squamous epithelium (SSE) along the basal-luminal axis

Figure 2.

Systematic profiling of Keratin code along the proximal-distal and basal-luminal axes

Basal cells exhibit transcriptomic regional diversity with unique functional signatures

Figure 3.

Submucosal gland cell heterogeneity as revealed at single cell resolution

Figure 4.

Luminal cell heterogeneity as revealed at single cell resolution

Figure 5.

Discussion

Limitations of the study

Resource availability

Lead contact

Materials availability

Data and code availability

Acknowledgments

Author contributions

Declaration of interests

STAR★Methods

Key resources table

Experimental model and study participant details

Method details

Tissue collection and immunofluorescence staining

RNAscope in situ hybridization

Tissue processing and flow cytometry

Quantification and statistical analysis

Preprocessing of 10X chromium scRNAseq data

Dimensionality reduction using PCA and UMAP

Integration, clustering, and downstream analysis

Footnotes

Supplemental information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases